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The popular and scientific literature has been discussing the advent of `big data' with a measure of excitement and apprehension. For the first time in history, it seems, every breath we take, every move we make, someone's watching us. But beyond their unprecedented volumes and the anxieties they raise, new communication data have a less obvious aspect, in so far as they are (arguably) of a fundamentally different \textit{kind}, compared to traditional network datasets.

Traditionally, social network data describe relationships between individuals; quasi-static social ties such as friendship, trust, kinship and employment relations. But when they are used to model digitally mediated communicative transactions, the connections are of a different nature. Instead of representing stable social ties, transactions (such as emails, text messages and phone calls) constitute sequences of short-lived events, with each transaction being a possible response to a preceding one and a potential stimulus to the next.
 
The point of departure of this dissertation is the distinction between the topology of the tie structure and the temporal structure of sequences of communicative transactions. Theoretically, the dissertation explores mechanisms of co-evolution between these two structures at three levels of aggregation: (i) the \textit{macro-level} consisting of the network itself or substructures within it, the level of an organization or a community as a whole; (ii) the \textit{meso-level} consisting of nodes and social ties; and (iii) the \textit{micro-level} consisting of sequences of interrelated communicative transactions. On the one hand, networks, individuals and ties are seen as the backdrop against which sequences of transactions unfold. On the other hand, transactions are considered to have (cumulative) consequences on the evolving structure of social ties and the network at large.

%Methodologically, the thesis is guided by a simple question: given a dataset of communication transactions within an organization, what are alternative methods to impute the meso-level of social ties? Two email datasets are explored, one reflecting email communication within a large firm, the other within a university setting. The problem is how to incorporate relevant information from the original dataset into the network model, so that the model carries more explanatory power. Three different approaches to the construction of network models are examined: the use of the strength of ties, a bipartite approach, and a multilevel statistical model with crossed random factors. A comparison between the methods reveals that different types of transactions expose various topologies of tie structures, the same group of interacting individuals mapped onto multiple relational topologies.

Methodologically, the thesis uses a publicly available dataset consisting of email transactions within Enron, an American energy and services company, during the few months of its bankruptcy. Two methods are applied to identify and explore the mechanisms. First, the dataset is disaggregated into various types of email transactions, revealing how different transactions contribute to various structural properties of the network. Second, a multilevel analysis approach is used to reveal how structural and transactional mechanisms combine to elicit new communicative transactions on the part of email recipients. 

The mechanisms identified in the empirical chapters challenge received wisdom about the nature of social networks and their link to the notion of social (trans)action while at the same time addressing practical problems faced by network modellers who need to construct networks out of digitally mediated transaction datasets. In addition, the findings raise general questions about new types of data and the consequences they may have, not only for the field of social networks, but also for popular ways of thinking about `the social' and ways of intervening in its course.






















%A great variety of systems in nature, society and technology—from the web of sexual contacts to the Internet, from the nervous system to power grids—can be modeled as graphs of vertices coupled by edges. The network structure, describing how the graph is wired, helps us understand, predict and optimize the behavior of dynamical systems. In many cases, however, the edges are not continuously active. As an example, in networks of communication via email, text messages, or phone calls, edges represent sequences of instantaneous or practically instantaneous contacts. In some cases, edges are active for non-negligible periods of time: e.g., the proximity patterns of inpatients at hospitals can be represented by a graph where an edge between two individuals is on throughout the time they are at the same ward. Like network topology, the temporal structure of edge activations can aect dynamics of systems interacting through the network, from disease contagion on the network of patients to information diusion over an e-mail network. In this review, we present the emergent field of temporal networks, and discuss methods for analyzing topological and temporal structure and models for elucidating their relation to the behavior of dynamical systems. In the light of traditional network theory, one can see this framework as moving the information of when things happen from the dynamical system on the network, to the network itself. Since fundamental properties, such as the transitivity of edges, do not necessarily hold in temporal networks, many of these methods need to be quite dierent from those for static networks. The study of temporal networks is very interdisciplinary in nature


%Reading through the history of the field of social networks, one may conclude that innovations in Information and Communication Technologies (ICTs) have had a profound impact on the field, but not in the way one might expect. Numerous commentators recognize that ICTs have opened vast new landscapes of data for exploration. However what is less obvious is that the difference between contemporary forms of relational data (communication, interaction, etc.) and traditional network data (friendship, kinship etc.) is not only one of degree, but also one of kind. This paper identifies these differences and the implications for the field in terms of its analytical framework, the profile of researchers it attracts and the kind of questions they are interested in.

%. My dissertation focuses on the links between networks of social relationships (e.g., friendship, kinship) and networks of social transactions (e.g., email, telecommunication). The tension between these two types of networks has attracted interest from the very first scholarly investigations of social networks.1 However, the need to understand the mutual influences between these phenomena has become even more pressing today given the availability of large datasets of social transactions compared to the relatively small and costly datasets of social relationships.2 Consequently, a growing literature2-11 strives to span these ‘two very different ways of characterizing the world’.3 It is within this literature that I situate my work. 

%The dissertation follows a mechanism based approach12 to explore the mutual influences between networks (macro-level), nodes and relationships (meso-level) and transactions (micro-level): on the one hand, nodes and relationships are seen as the background against which sequences of transactions unfold. On the other hand, transactions are considered to have (cumulative) consequences on the maintenance and evolution of social relationships. While previous studies7,15 of co-evolution in social networks have considered processes of selection13, influence14 and other changes in tie properties7 to explain the evolution of network topology, the dissertation explores how social transactions might maintain or affect properties of ties and nodes. 


%One of the manifestations of these shifts has been succinctly captured in the title of a recent book review by sociologist Phillip Bonacich, The invasion of the physicists. Indeed, today numerous science and technology research centers invest considerable effort in the study of interpersonal interaction and communication practices, objects of research that were until recently within the exclusive purview of perhaps the most phenomenologically-oriented fields in sociology and anthropology. But since the end of the 1990s, physicists, engineers and mathematicians have been ‘rediscovering’, quoting and applying concepts originally introduced by sociologists and psychologists between the 1940s and the 1970s. What is remarkable about these developments is not only the rapid entrance of a new breed of scholars into the canon of social network literature, but also the process by which their work feeds back and influences mainstream papers in sociology. Conceptually, these developments facilitate the reconfiguration of the boundaries between the social and the artificial, between the private and the public spheres. They involve a growing fascination with the impact of the ‘materiality’ of mediation and an increasingly heated debate over the meaning of ‘scale’ in socio-technical systems.  

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